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Record W2886176480 · doi:10.1377/hlthaff.2018.0221

Prices For Common Cardiovascular Drugs In The US Are Not Consistently Aligned With Value

2018· article· en· W2886176480 on OpenAlexaff
Jonathan D. Campbell, Vasily Belozeroff, Melanie D. Whittington, Robert J. Rubin, Paolo Raggi, Andrew Briggs

Bibliographic record

VenueHealth Affairs · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealth Systems, Economic Evaluations, Quality of Life
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReimbursementActuarial scienceValue (mathematics)Cost effectivenessCost–benefit analysisHealth careDrug pricesValue-Based PurchasingPrincipal (computer security)EconomicsMedicinePublic economicsBusinessOperations managementComputer science

Abstract

fetched live from OpenAlex

Health care reimbursement agencies in countries other than the US often rely on cost-effectiveness evidence for drug coverage decisions, signaling to drug manufacturers their expectations for value-based pricing. To see whether drug prices in the US are influenced by value, we estimated the range of cost-effectiveness for thirty frequently prescribed cardiovascular drugs. We extrapolated evidence from randomized controlled trials to determine average lifetime quality-adjusted life-years (QALYs) and payer-related costs and to calculate incremental cost-effectiveness ratios (ICERs), the principal metric of cost-effectiveness studies. Across the thirty drugs, the ICERs ranged from cost-saving with increased QALYs to more costly with decreased QALYs. This range suggests that drug pricing is not consistently influenced by value, or that such influence is masked by inaccessible factors, such as price discounts. Our findings highlight the need to debate how to define and use value-based evidence to inform US coverage and reimbursement decision making.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.564
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.209
GPT teacher head0.395
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2018
Admission routes1
Has abstractyes

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